Multivariate Modelling Of Land Use On In-stream Salinity Over Multiple Spatial Scales
Free (open access)
V. Versace, D. Ierodiaconou, S. Salzman, F. Stagnitti, M. Leblanc, A. Boland, L. Laurenson, T. March & L. Thwaites
An impediment to sustainable dryland salinity management is the lack of information on contributing factors. GIS and satellite imagery now offer a cost-effective means of generating relevant land and water resource information for integrated regional management of salinity. In this paper the relationships between patterns in land use/cover distribution and base flow salt concentration in streams (indicated by EC) are investigated and modelled. The Glenelg-Hopkins area is a large regional watershed in southwest Victoria, Australia, covering approximately 2.6 million ha. It is currently estimated that 27,400 ha of land is affected by dryland salinity and this is predicted to rapidly increase in the next decade if current conditions prevail. Salt concentration data from five gauging stations were analysed with multi-temporal land use maps obtained from satellite imagery. Multiple regression analyses demonstrated that the variables Native Vegetation and Dryland Grain Cropping were the most significant influences on in-stream salinity in the whole catchment (r2=88.9%) and 500 m (r2=88.3%) and 100 m riparian buffers (r2=86.9%) during times of base flow. The implications for future land use planning, effectiveness of riparian zones and revegetation programmes is discussed. This work also demonstrates the utility of applying multivariate statistical analyses, spatial statistics, and remote sensing with data integrated in a GIS framework for the purpose of predicting and managing the regional salinity threat. Keywords: dryland salinity, salt concentration, remote sensing, multiple regression, land use.
dryland salinity, salt concentration, remote sensing, multipleregression, land use.